This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

⏪ | 🏠 | ⏩

Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Trap TrapFish 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl TrawFish 2013-2014 2 Pandalus borealis
Net NetFish 2010 5 Clupea harengus, Gadus morhua
Dredge DredFish 2010-2014 21 Mactromeris polynyma

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

AquaInf
## Model selected: Lin
## nugget = 0; sill = 0.00409; range = 1.98608; kappa = 0.5

CityInf
## Model selected: Lin
## nugget = 0; sill = 0.03491; range = 8.85176; kappa = 0.5

InduInf
## Model selected: Lin
## nugget = 0; sill = 0.04594; range = 7.94625; kappa = 0.5

CollDred
## Model selected: Exp
## nugget = 0.00115; sill = 0.00567; range = 6.36284; kappa = 0.5

DumpDred
## Model selected: Lin
## nugget = 0; sill = 0.0084; range = 1.14583; kappa = 0.5

MoorShip
## Model selected: Lin
## nugget = 0; sill = 0.07595; range = 3.05577; kappa = 0.5

TrafShip
## Model selected: Exp
## nugget = 0; sill = 0.22432; range = 3.01617; kappa = 0.5

RainSew
## Model selected: Lin
## nugget = 0; sill = 0.01386; range = 7.93527; kappa = 0.5

WastSew
## Model selected: Sph
## nugget = 0; sill = 0.0132; range = 23.19017; kappa = 0.5

CityWha
## Model selected: Exp
## nugget = 0; sill = 0.01242; range = 7.08521; kappa = 0.5

InduWha
## Model selected: Sph
## nugget = 0.00029; sill = 0.0083; range = 5.59199; kappa = 0.5

TrapFish
## Model selected: Lin
## nugget = 0.00033; sill = 0.00125; range = 1.13171; kappa = 0.5

TrawFish
## Model selected: Lin
## nugget = 0; sill = 0.03496; range = 3.90865; kappa = 0.5

NetFish
## Model selected: Exp
## nugget = 0; sill = 0.00405; range = 0.7045; kappa = 0.5

DredFish
## Model selected: Lin
## nugget = 0; sill = 0.0102; range = 2.82184; kappa = 0.5

2. Relationships between exposure indices and abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

AquaInf

CityInf

InduInf

CollDred

DumpDred

MoorShip

TrafShip

RainSew

WastSew

CityWha

InduWha

TrapFish

TrawFish

NetFish

DredFish

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N H J
aquaculture -0.1158 -0.02547 -0.04022 0.05245 -0.001245 -0.1348 -0.1808 -0.2441 -0.2756 -0.2226 -0.1868 -0.1541 -0.2108 -0.2589 0.214 0.01476 0.1318 0.00835
city -0.1915 -0.1374 0.3251 -0.2108 -0.07314 -0.2814 -0.1124 -0.1374 0.09102 0.1144 -0.2531 -0.2167 -0.1365 0.022 -0.1389 0.08568 -0.1017 -0.01358
dredging_collect 0.1284 -0.04929 0.03491 0.007727 -0.03208 -0.02709 -0.07619 -0.007967 0.08903 0.443 0.1368 -0.07797 -0.04447 0.04908 -0.08664 -0.07784 0.01154 0.07244
dredging_dump 0.172 -0.04662 -0.1423 0.1796 -0.03045 0.49 0.0951 0.1621 0.1948 0.1584 0.1837 0.05096 0.1757 0.1812 -0.1103 -0.1077 0.001877 0.06612
industry 0.1108 -0.06908 0.1182 -0.08383 0.002292 0.2177 -0.08365 0.2081 0.346 0.5225 0.4589 -0.04507 0.08678 0.171 -0.2597 -0.1106 -0.1693 -0.06869
shipping_mooring 0.1507 -0.0584 -0.2178 0.2821 -0.07104 0.2276 0.3382 0.2542 0.2608 0.08722 0.1473 0.5368 0.3707 0.3189 0.04762 -0.02356 0.01182 -0.04485
shipping_traffic 0.2781 -0.1757 -0.1499 0.2059 0.04646 0.2906 0.12 0.2351 0.3716 0.4034 0.2098 0.2713 0.2493 0.3046 -0.07039 -0.1667 0.02405 0.04157
sewers_rain 0.2638 -0.03807 -0.3391 0.2332 0.208 0.6053 0.3381 0.5494 0.5711 0.3675 0.6572 0.2872 0.5263 0.4762 -0.2744 -0.02765 -0.2634 -0.1792
sewers_waste 0.2178 -0.01654 -0.2016 0.1443 0.109 0.5704 0.4656 0.5462 0.5874 0.3012 0.5258 0.3444 0.6146 0.5542 -0.3849 0.09536 -0.3498 -0.1482
wharves_city -0.04686 -0.07177 0.156 -0.09151 -0.04696 -0.1338 -0.03555 0.03123 0.1656 0.1915 -0.04004 -0.1075 -0.04788 0.1061 -0.05345 -0.0373 -0.0008743 0.02854
wharves_industry 0.1701 -0.01676 0.04789 -0.02939 -0.01672 0.3624 -0.07242 0.2356 0.2978 0.6577 0.4764 0.01584 0.1617 0.1531 -0.3295 -0.1381 -0.2458 -0.08293
fisheries_trap -0.2583 -0.04088 0.2419 -0.1873 -0.05337 -0.1959 -0.063 -0.1121 -0.0935 -0.09971 -0.1092 -0.1393 -0.1544 -0.1124 0.01116 0.05966 -0.02473 -0.04636
fisheries_trawl -0.1915 0.2673 0.05775 -0.1588 -0.04853 -0.1297 -0.1941 -0.1716 -0.1947 -0.1103 -0.1688 -0.155 -0.1798 -0.1872 0.1712 0.05158 0.00763 -0.1071
fisheries_net 0.07494 -0.02727 -0.05045 0.07243 -0.01724 -0.002186 -0.01307 0.01643 0.01172 0.01391 0.01635 -0.01842 0.001042 0.003464 -0.01593 -0.03741 0.02955 0.03216
fisheries_dredge -0.1612 -0.003513 0.2096 -0.1735 -0.05351 -0.2004 -0.2427 -0.3064 -0.3385 -0.2843 -0.2684 -0.2206 -0.2788 -0.3069 0.3319 0.01544 0.2737 0.1183
cumulative_exposure 0.2362 -0.1483 -0.09073 0.1678 -0.01221 0.4003 0.1526 0.3345 0.5125 0.5732 0.3907 0.2753 0.3375 0.4036 -0.1838 -0.1344 -0.1049 -0.04512

3. Species abundances by cumulative exposure index

The following graphs present the distribution of sampled phyla along a gradient of cumulative exposure.

The threshold classification is based on the exposure index: the higher the index, the lower the status.

Phylum mean abundances by group
Phylum low bad moderate high good
Annelida 8 15 28.6 41.7 29.4
Arthropoda 9.5 12.3 37 61.8 45.5
Cnidaria 0 0 0 0 0.0161
Echinodermata 0.5 1 0.115 5 3.92
Mollusca 24 21 7.19 8.4 16.9
Nematoda 0 0 0.423 1.8 16.2
Nemertea 0 0 0.154 0 0.194
Sipuncula 1 0 0.5 0.333 0.145

4. Regressions between exposure indices and community characteristics

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

All stations and predictors were selected for the regressions, as we are interested in each of them.

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the table below:

Variable (or combination) S N H J Annelids Arthopods Molluscs
AquaInf ** ** ** ** ** ** ** ** ** ** ** ** ** **
CityInf ** ** ** ** ** ** ** ** ** ** ** ** ** **
InduInf ** ** ** ** ** ** ** ** ** ** ** ** ** **
CollDred ** ** ** ** ** ** ** ** ** ** ** ** ** **
DumpDred ** ** ** ** ** ** ** ** ** ** ** ** ** **
MoorShip ** ** ** ** ** ** ** ** ** ** ** ** ** **
TrafShip ** ** ** ** ** ** ** ** ** ** ** ** ** **
RainSew ** ** ** ** ** ** ** ** ** ** ** ** ** **
WastSew ** ** ** ** ** ** ** ** ** ** ** ** ** **
CityWha ** ** ** ** ** ** ** ** ** ** ** ** ** **
InduWha ** ** ** ** ** ** ** ** ** ** ** ** ** **
TrapFish ** ** ** ** ** ** ** ** ** ** ** ** ** **
TrawFish ** ** ** ** ** ** ** ** ** ** ** ** ** **
NetFish ** ** ** ** ** ** ** ** ** ** ** ** ** **
DredFish ** ** ** ** ** ** ** ** ** ** ** ** ** **
Adjusted \(R^{2}\)

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.19
Fitting linear model: S ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.756e-17 0.0865 4.342e-16 1
aquaculture 0.1492 0.09442 1.581 0.1174
city -0.2279 0.191 -1.193 0.2359
dredging_collect 0.0811 0.1256 0.6457 0.5201
dredging_dump -0.07032 0.09465 -0.743 0.4594
industry 0.4327 0.2262 1.913 0.05886
shipping_mooring 0.05063 0.1168 0.4336 0.6656
shipping_traffic 0.09784 0.1273 0.7689 0.444
sewers_rain -0.1481 0.1735 -0.8533 0.3957
sewers_waste -0.02823 0.152 -0.1856 0.8531
wharves_city 0.01368 0.1586 0.08629 0.9314
wharves_industry -0.5888 0.2096 -2.809 0.00607 * *
fisheries_trap 0.02205 0.1147 0.1923 0.8479
fisheries_trawl 0.1541 0.09521 1.619 0.1089
fisheries_net -0.02756 0.0911 -0.3025 0.763
fisheries_dredge 0.2589 0.09578 2.704 0.008172 * * 
## RMSE from cross-validation: 74.49011
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.09 2.2 1.45 1.09 2.6 1.34 1.46 2 1.75 1.82 2.41 1.32 1.1 1.05 1.1 

## REDUCED MODEL
## Adjusted R2 is: 0.23
Fitting linear model: S ~ aquaculture + city + industry + sewers_rain + wharves_industry + fisheries_trawl + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.518e-17 0.08451 -1.796e-16 1
aquaculture 0.1495 0.0894 1.672 0.09765
city -0.1757 0.1051 -1.672 0.09767
industry 0.4391 0.1887 2.326 0.02202 *
sewers_rain -0.1721 0.1211 -1.421 0.1584
wharves_industry -0.5534 0.1674 -3.306 0.001314 * *
fisheries_trawl 0.147 0.08958 1.641 0.104
fisheries_dredge 0.2472 0.09106 2.715 0.007802 * * 
## RMSE from cross-validation: 0.9005653
Variance Inflation Factors
  aquaculture city industry sewers_rain wharves_industry fisheries_trawl fisheries_dredge
VIF 1.05 1.24 2.22 1.43 1.97 1.05 1.07

Density
## FULL MODEL
## Adjusted R2 is: -0.04
Fitting linear model: N ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.613e-16 0.09806 3.685e-15 1
aquaculture 0.04529 0.107 0.4231 0.6732
city 0.2669 0.2165 1.233 0.2208
dredging_collect 0.03992 0.1424 0.2804 0.7798
dredging_dump -0.08565 0.1073 -0.7982 0.4268
industry 0.00836 0.2564 0.0326 0.9741
shipping_mooring 0.05349 0.1324 0.4041 0.6871
shipping_traffic -0.1616 0.1443 -1.12 0.2654
sewers_rain 0.1503 0.1967 0.7638 0.4469
sewers_waste 0.1219 0.1724 0.7071 0.4813
wharves_city -0.2007 0.1798 -1.117 0.267
wharves_industry -0.2085 0.2377 -0.8772 0.3826
fisheries_trap 0.0419 0.13 0.3223 0.7479
fisheries_trawl 0.0994 0.1079 0.9209 0.3595
fisheries_net -0.01414 0.1033 -0.1369 0.8914
fisheries_dredge 0.04752 0.1086 0.4377 0.6627 
## RMSE from cross-validation: 67.53029
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.09 2.2 1.45 1.09 2.6 1.34 1.46 2 1.75 1.82 2.41 1.32 1.1 1.05 1.1 

## REDUCED MODEL
## Adjusted R2 is: 0.04
Fitting linear model: N ~ shipping_traffic + sewers_waste + wharves_industry
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.523e-16 0.09417 2.679e-15 1
shipping_traffic -0.1538 0.09713 -1.584 0.1163
sewers_waste 0.192 0.1038 1.849 0.06737
wharves_industry -0.1829 0.1054 -1.736 0.08557 
## RMSE from cross-validation: 1.024639
Variance Inflation Factors
  shipping_traffic sewers_waste wharves_industry
VIF 1.03 1.1 1.11

Diversity
## FULL MODEL
## Adjusted R2 is: 0.13
Fitting linear model: H ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.367e-16 0.08997 5.966e-15 1
aquaculture 0.0483 0.09821 0.4918 0.624
city -0.4247 0.1987 -2.138 0.0352 *
dredging_collect 0.1413 0.1306 1.081 0.2824
dredging_dump 0.01613 0.09845 0.1639 0.8702
industry 0.4705 0.2353 2 0.04847 *
shipping_mooring -0.06062 0.1215 -0.4991 0.6189
shipping_traffic 0.1862 0.1324 1.407 0.1629
sewers_rain -0.3528 0.1805 -1.955 0.05366
sewers_waste 0.02754 0.1582 0.1741 0.8622
wharves_city 0.1115 0.1649 0.676 0.5007
wharves_industry -0.5602 0.218 -2.569 0.01181 *
fisheries_trap 0.0004403 0.1193 0.003691 0.9971
fisheries_trawl -0.04307 0.09903 -0.4349 0.6646
fisheries_net 0.008452 0.09476 0.0892 0.9291
fisheries_dredge 0.1905 0.09963 1.912 0.059 
## RMSE from cross-validation: 3.365337
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.09 2.2 1.45 1.09 2.6 1.34 1.46 2 1.75 1.82 2.41 1.32 1.1 1.05 1.1 

## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: H ~ city + dredging_collect + industry + shipping_traffic + sewers_rain + wharves_industry + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.545e-16 0.08699 6.374e-15 1
city -0.3272 0.1137 -2.878 0.004894 * *
dredging_collect 0.1677 0.1102 1.522 0.1311
industry 0.4759 0.1927 2.47 0.0152 *
shipping_traffic 0.158 0.09467 1.669 0.09818
sewers_rain -0.3095 0.1258 -2.46 0.0156 *
wharves_industry -0.5618 0.1791 -3.137 0.002243 * *
fisheries_dredge 0.2123 0.09302 2.282 0.0246 * 
## RMSE from cross-validation: 0.9913484
Variance Inflation Factors
  city dredging_collect industry shipping_traffic sewers_rain wharves_industry fisheries_dredge
VIF 1.3 1.26 2.2 1.08 1.44 2.05 1.06

Evenness
## FULL MODEL
## Adjusted R2 is: -0.02
Fitting linear model: J ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.173e-17 0.09741 -4.284e-16 1
aquaculture -0.06583 0.1063 -0.6192 0.5373
city -0.3493 0.2151 -1.624 0.1078
dredging_collect 0.116 0.1414 0.8198 0.4144
dredging_dump 0.05936 0.1066 0.5569 0.5789
industry 0.1579 0.2547 0.6198 0.5369
shipping_mooring -0.1499 0.1315 -1.14 0.2573
shipping_traffic 0.1542 0.1433 1.076 0.2847
sewers_rain -0.3763 0.1954 -1.925 0.05727
sewers_waste 0.08788 0.1712 0.5132 0.609
wharves_city 0.1238 0.1786 0.6933 0.4899
wharves_industry -0.185 0.2361 -0.7835 0.4353
fisheries_trap -0.02202 0.1291 -0.1705 0.865
fisheries_trawl -0.1756 0.1072 -1.637 0.105
fisheries_net 0.0213 0.1026 0.2077 0.836
fisheries_dredge 0.05376 0.1079 0.4984 0.6194 
## RMSE from cross-validation: 100.5167
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.09 2.2 1.45 1.09 2.6 1.34 1.46 2 1.75 1.82 2.41 1.32 1.1 1.05 1.1 

## REDUCED MODEL
## Adjusted R2 is: 0.05
Fitting linear model: J ~ city + shipping_mooring + shipping_traffic + sewers_rain + fisheries_trawl
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.498e-16 0.09403 -1.593e-15 1
city -0.168 0.1071 -1.569 0.1198
shipping_mooring -0.174 0.1123 -1.549 0.1245
shipping_traffic 0.1939 0.1177 1.648 0.1024
sewers_rain -0.2968 0.1068 -2.778 0.006505 * *
fisheries_trawl -0.1724 0.09938 -1.735 0.08583 
## RMSE from cross-validation: 1.077156
Variance Inflation Factors
  city shipping_mooring shipping_traffic sewers_rain fisheries_trawl
VIF 1.13 1.19 1.25 1.13 1.05

Annelids
## FULL MODEL
## McFadden's pseudo-R2 is: 0.11
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.327 0.01918 173.5 0 * * *
aquaculture 0.1188 0.01618 7.346 2.036e-13 * * *
city -0.006262 0.04007 -0.1563 0.8758
dredging_collect -0.08911 0.02979 -2.992 0.002775 * *
dredging_dump -0.09399 0.02678 -3.51 0.0004481 * * *
industry 0.1312 0.05177 2.534 0.01126 *
shipping_mooring 0.1237 0.02257 5.483 4.187e-08 * * *
shipping_traffic -0.1175 0.02577 -4.56 5.124e-06 * * *
sewers_rain -0.007578 0.03741 -0.2025 0.8395
sewers_waste 0.1423 0.034 4.186 2.834e-05 * * *
wharves_city 0.09572 0.02678 3.574 0.000351 * * *
wharves_industry -0.3646 0.0562 -6.488 8.7e-11 * * *
fisheries_trap 0.02175 0.02053 1.059 0.2894
fisheries_trawl -0.1876 0.03243 -5.786 7.217e-09 * * *
fisheries_net -0.03257 0.02348 -1.387 0.1653
fisheries_dredge -0.05999 0.02418 -2.481 0.01311 * 
## Unbiased RMSE from cross-validation: 38.57226
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.16 2.48 1.21 1.04 2.35 1.4 1.41 1.84 1.86 2.05 2.13 1.62 1.09 1.03 1.15 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.11
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_waste + wharves_city + wharves_industry + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.327 0.01917 173.6 0 * * *
aquaculture 0.122 0.01579 7.726 1.11e-14 * * *
dredging_collect -0.09987 0.02712 -3.682 0.0002311 * * *
dredging_dump -0.09135 0.0264 -3.46 0.0005393 * * *
industry 0.1326 0.04374 3.031 0.002438 * *
shipping_mooring 0.1269 0.02164 5.864 4.513e-09 * * *
shipping_traffic -0.1261 0.02378 -5.302 1.147e-07 * * *
sewers_waste 0.1385 0.02244 6.174 6.642e-10 * * *
wharves_city 0.111 0.01468 7.563 3.933e-14 * * *
wharves_industry -0.3651 0.05007 -7.291 3.086e-13 * * *
fisheries_trawl -0.1858 0.03214 -5.78 7.46e-09 * * *
fisheries_net -0.03143 0.02322 -1.354 0.1758
fisheries_dredge -0.05812 0.02396 -2.426 0.01526 * 
## Unbiased RMSE from cross-validation: 40.88161
Variance Inflation Factors
  aquaculture dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_waste wharves_city wharves_industry fisheries_trawl fisheries_net fisheries_dredge
VIF 1.13 1.11 1.02 1.99 1.34 1.3 1.23 1.12 1.89 1.08 1.02 1.14

Arthropods
## FULL MODEL
## McFadden's pseudo-R2 is: 0.25
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.566 0.01774 201 0 * * *
aquaculture 0.001736 0.02192 0.07922 0.9369
city 0.2493 0.02899 8.599 8.065e-18 * * *
dredging_collect 0.06636 0.03109 2.134 0.03283 *
dredging_dump -0.2278 0.03006 -7.58 3.468e-14 * * *
industry 0.2236 0.04455 5.018 5.212e-07 * * *
shipping_mooring 0.1898 0.01885 10.07 7.635e-24 * * *
shipping_traffic -0.2288 0.02117 -10.81 3.149e-27 * * *
sewers_rain 0.3584 0.02368 15.13 9.583e-52 * * *
sewers_waste 0.4245 0.02501 16.98 1.244e-64 * * *
wharves_city -0.2906 0.03567 -8.145 3.782e-16 * * *
wharves_industry -0.6805 0.0462 -14.73 4.195e-49 * * *
fisheries_trap -0.03652 0.01785 -2.046 0.04072 *
fisheries_trawl 0.1233 0.01579 7.811 5.668e-15 * * *
fisheries_net -0.03258 0.02067 -1.576 0.115
fisheries_dredge 0.1307 0.01364 9.581 9.595e-22 * * * 
## Unbiased RMSE from cross-validation: 247.6144
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.07 2.04 1.3 1.02 2.72 1.36 1.33 1.82 1.96 1.53 2.61 1.12 1.12 1.02 1.1 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.25
Fitting generalized (poisson/log) linear model: arthropods ~ city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.566 0.01774 201 0 * * *
city 0.2491 0.0289 8.622 6.607e-18 * * *
dredging_collect 0.06645 0.03107 2.139 0.03246 *
dredging_dump -0.2279 0.03006 -7.582 3.408e-14 * * *
industry 0.2228 0.04355 5.116 3.118e-07 * * *
shipping_mooring 0.1896 0.01866 10.16 2.977e-24 * * *
shipping_traffic -0.2287 0.02116 -10.81 3.139e-27 * * *
sewers_rain 0.3584 0.02367 15.14 9.297e-52 * * *
sewers_waste 0.4243 0.02487 17.06 2.93e-65 * * *
wharves_city -0.2905 0.03566 -8.146 3.747e-16 * * *
wharves_industry -0.6798 0.04545 -14.96 1.417e-50 * * *
fisheries_trap -0.03635 0.01771 -2.052 0.04018 *
fisheries_trawl 0.1231 0.01561 7.885 3.145e-15 * * *
fisheries_net -0.03263 0.02066 -1.579 0.1142
fisheries_dredge 0.1307 0.01364 9.586 9.155e-22 * * * 
## Unbiased RMSE from cross-validation: 119.3895
Variance Inflation Factors
  city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 2.03 1.3 1.02 2.66 1.34 1.33 1.82 1.95 1.53 2.57 1.11 1.1 1.02 1.1

Molluscs
## FULL MODEL
## McFadden's pseudo-R2 is: 0.27
Fitting generalized (poisson/log) linear model: molluscs ~ aquaculture + city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.401 0.03209 74.82 0 * * *
aquaculture 0.1329 0.02251 5.902 3.595e-09 * * *
city 0.4958 0.06196 8.001 1.234e-15 * * *
dredging_collect 0.0555 0.03201 1.734 0.08292
dredging_dump -0.1538 0.05527 -2.783 0.005385 * *
industry 0.464 0.06055 7.663 1.82e-14 * * *
shipping_mooring -0.02887 0.04629 -0.6237 0.5328
shipping_traffic -0.1967 0.04477 -4.394 1.111e-05 * * *
sewers_rain 0.01542 0.05418 0.2847 0.7759
sewers_waste -0.4035 0.05628 -7.169 7.542e-13 * * *
wharves_city -0.3381 0.04519 -7.482 7.329e-14 * * *
wharves_industry -0.3206 0.06183 -5.185 2.156e-07 * * *
fisheries_trap 0.0924 0.02728 3.387 0.0007066 * * *
fisheries_trawl 0.09371 0.02449 3.826 0.0001305 * * *
fisheries_net 0.06714 0.027 2.486 0.0129 *
fisheries_dredge 0.12 0.01743 6.887 5.696e-12 * * * 
## Unbiased RMSE from cross-validation: 87.68681
Variance Inflation Factors
  aquaculture city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 1.14 2.71 1.72 1.01 2.38 1.24 1.53 1.68 1.73 2.09 2.03 1.27 1.1 1.08 1.09 

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.25
Fitting generalized (poisson/log) linear model: arthropods ~ city + dredging_collect + dredging_dump + industry + shipping_mooring + shipping_traffic + sewers_rain + sewers_waste + wharves_city + wharves_industry + fisheries_trap + fisheries_trawl + fisheries_net + fisheries_dredge
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.566 0.01774 201 0 * * *
city 0.2491 0.0289 8.622 6.607e-18 * * *
dredging_collect 0.06645 0.03107 2.139 0.03246 *
dredging_dump -0.2279 0.03006 -7.582 3.408e-14 * * *
industry 0.2228 0.04355 5.116 3.118e-07 * * *
shipping_mooring 0.1896 0.01866 10.16 2.977e-24 * * *
shipping_traffic -0.2287 0.02116 -10.81 3.139e-27 * * *
sewers_rain 0.3584 0.02367 15.14 9.297e-52 * * *
sewers_waste 0.4243 0.02487 17.06 2.93e-65 * * *
wharves_city -0.2905 0.03566 -8.146 3.747e-16 * * *
wharves_industry -0.6798 0.04545 -14.96 1.417e-50 * * *
fisheries_trap -0.03635 0.01771 -2.052 0.04018 *
fisheries_trawl 0.1231 0.01561 7.885 3.145e-15 * * *
fisheries_net -0.03263 0.02066 -1.579 0.1142
fisheries_dredge 0.1307 0.01364 9.586 9.155e-22 * * * 
## Unbiased RMSE from cross-validation: 156.6728
Variance Inflation Factors
  city dredging_collect dredging_dump industry shipping_mooring shipping_traffic sewers_rain sewers_waste wharves_city wharves_industry fisheries_trap fisheries_trawl fisheries_net fisheries_dredge
VIF 2.03 1.3 1.02 2.66 1.34 1.33 1.82 1.95 1.53 2.57 1.11 1.1 1.02 1.1

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